






"""simple docstring"""



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
						import tensorflow as tf
						from tensorflow.python.eager import context
						from tensorflow.python.framework import ops

						from transformers import GradientAccumulator, create_optimizer





@require_tf
class    _UpperCAmelCase			(					unittest.TestCase				):




					def     A    (							self	:		int       ,		A	:		int       ,		A	:		Any       ,		A	:		Dict  )    ->    str:
							self.assertEqual(len(__SCREAMING_SNAKE_CASE  )       ,		len(__SCREAMING_SNAKE_CASE  )  )
							for a, b in zip(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  ):
									self.assertAlmostEqual(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		delta=__SCREAMING_SNAKE_CASE  )




					def     A    (							self	:		List[Any]  )    ->    int:
							lowercase_					:					Optional[int]							=  GradientAccumulator()
							accumulator([tf.constant([1.0, 2.0]  )]  )
							accumulator([tf.constant([-2.0, 1.0]  )]  )
							accumulator([tf.constant([-1.0, 2.0]  )]  )
							with self.assertRaises(__SCREAMING_SNAKE_CASE  ):
									accumulator([tf.constant([1.0, 1.0]  ), tf.constant([2.0, 2.0]  )]  )
							self.assertEqual(accumulator.step       ,		3  )
							self.assertEqual(len(accumulator.gradients  )       ,		1  )
							self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()       ,		[-2.0, 5.0]       ,		tol=1e-2  )
							accumulator.reset()
							self.assertEqual(accumulator.step       ,		0  )
							self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()       ,		[0.0, 0.0]       ,		tol=1e-2  )






					def     A    (							self	:		str  )    ->    Any:
							lowercase_					:					Tuple							=  None
							ops.enable_eager_execution_internal()
							lowercase_					:					Tuple							=  tf.config.list_physical_devices('''CPU'''  )
							if len(__SCREAMING_SNAKE_CASE  ) == 1:
									tf.config.set_logical_device_configuration(
									    physical_devices[0]       ,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]  )
							lowercase_					:					List[str]							=  tf.config.list_logical_devices(device_type='''CPU'''  )
							lowercase_					:					Optional[int]							=  tf.distribute.MirroredStrategy(devices=devices[:2]  )

							with strategy.scope():
									lowercase_					:					Optional[int]							=  GradientAccumulator()
									lowercase_					:					Tuple							=  tf.Variable([4.0, 3.0]  )
									lowercase_			,   lowercase_					:					Tuple							=  create_optimizer(5e-5       ,		10       ,		5  )
									lowercase_					:					Optional[int]							=  tf.Variable([0.0, 0.0]       ,		trainable=__SCREAMING_SNAKE_CASE  )

							def accumulate_on_replica(A	:		Union[str, Any]  ):
									accumulator([gradient]  )

							def apply_on_replica():
									optimizer.apply_gradients(list(zip(accumulator.gradients       ,		[variable]  )  )  )

							@tf.function
							def accumulate(A	:		Any       ,		A	:		Any  ):
									with strategy.scope():
											lowercase_					:					str							=  strategy.experimental_local_results(__SCREAMING_SNAKE_CASE  )
											local_variables[0].assign(__SCREAMING_SNAKE_CASE  )
											local_variables[1].assign(__SCREAMING_SNAKE_CASE  )
											strategy.run(__SCREAMING_SNAKE_CASE       ,		args=(gradient_placeholder,)  )

							@tf.function
							def apply_grad():
									with strategy.scope():
											strategy.run(__SCREAMING_SNAKE_CASE  )

							def _check_local_values(A	:		int       ,		A	:		List[str]  ):
									lowercase_					:					Tuple							=  strategy.experimental_local_results(accumulator._gradients[0]  )
									self.assertListAlmostEqual(values[0].value()       ,		__SCREAMING_SNAKE_CASE       ,		tol=1e-2  )
									self.assertListAlmostEqual(values[1].value()       ,		__SCREAMING_SNAKE_CASE       ,		tol=1e-2  )

							accumulate([1.0, 2.0]       ,		[-1.0, 1.0]  )
							accumulate([3.0, -1.0]       ,		[-1.0, -1.0]  )
							accumulate([-2.0, 2.0]       ,		[3.0, -2.0]  )
							self.assertEqual(accumulator.step       ,		3  )
							_check_local_values([2.0, 3.0]       ,		[1.0, -2.0]  )
							apply_grad()
							self.assertListAlmostEqual(variable.value()       ,		[4.0, 3.0]       ,		tol=1e-2  )
							accumulator.reset()
							self.assertEqual(accumulator.step       ,		0  )
							_check_local_values([0.0, 0.0]       ,		[0.0, 0.0]  )











"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




